Diabetes research and clinical practice · 2026
Continuous glucose monitoring for prediabetes classification in a large real-world cohort: Comparison with HbA1c and fasting plasma glucose.
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Rákóczi Gabriella, Csányi Dávid, Molontay Roland, Agócs Gergely, Szentes Boglárka Lilla, Lee Jimin, Rancz Anett, Hegyi Péter, Sipter Emese
Аннотация
AIMS: Prediabetes classification based on hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) shows substantial discordance, potentially limiting early identification of individuals at metabolic risk. We evaluated whether continuous glucose monitoring (CGM) metrics can classify prediabetes with performance approaching that of HbA1c and FPG.
METHODS: We conducted a cross-sectional analysis of 1,883 participants from the Human Phenotype Project with concurrent CGM, HbA1c, and FPG data. Individual CGM metrics were evaluated across four ADA-based laboratory reference definitions, and a combined model incorporating six clinically selected CGM metrics was additionally assessed. Discrimination was quantified using the area under the receiver operating characteristic curve (AUC).
RESULTS: HbA1c- and FPG-based classifications showed marked discordance, with only 9.5% of participants meeting both prediabetes criteria. Several CGM metrics demonstrated classification performance approaching that of laboratory markers (AUC range 0.645-0.677). Discrimination improved when prediabetes was defined by both criteria, but no single marker achieved high performance independently.
CONCLUSIONS: CGM metrics achieve classification performance approaching conventional laboratory markers while capturing complementary aspects of glycemic regulation. These findings support CGM as a complementary rather than standalone tool alongside conventional markers, particularly when laboratory results are discordant. Prospective studies incorporating oral glucose tolerance testing and longitudinal outcomes are warranted.
METHODS: We conducted a cross-sectional analysis of 1,883 participants from the Human Phenotype Project with concurrent CGM, HbA1c, and FPG data. Individual CGM metrics were evaluated across four ADA-based laboratory reference definitions, and a combined model incorporating six clinically selected CGM metrics was additionally assessed. Discrimination was quantified using the area under the receiver operating characteristic curve (AUC).
RESULTS: HbA1c- and FPG-based classifications showed marked discordance, with only 9.5% of participants meeting both prediabetes criteria. Several CGM metrics demonstrated classification performance approaching that of laboratory markers (AUC range 0.645-0.677). Discrimination improved when prediabetes was defined by both criteria, but no single marker achieved high performance independently.
CONCLUSIONS: CGM metrics achieve classification performance approaching conventional laboratory markers while capturing complementary aspects of glycemic regulation. These findings support CGM as a complementary rather than standalone tool alongside conventional markers, particularly when laboratory results are discordant. Prospective studies incorporating oral glucose tolerance testing and longitudinal outcomes are warranted.